Hadoop is being increasingly used by companies of diverse scope and size and they are realizing that running Hadoop optimally is a tough call. As a matter of fact it is not humanly possible to respond to the changing conditions in real time as these may take place across several nodes in order to fix dips in performance or those that are causing bottlenecks. This performance degradation is exactly what needsto be critically remedied in cases where Hadoop is deployed on large scales where Hadoop is expected to deliver results critical to your business in the proper time. The following three signs signal the health of your Hadoop cluster.
The Out of Capacity Problem
The true test of your Hadoop infrastructure comes to fore when you are able to efficiently run all of your jobs and complete them within adequate time. In this it is not rare to come across instances where you have seemingly run out of capacity as you are unable to run additional application. However monitoring tools indicate that are not making full use of processing capability or other resources. The primary challenge that now lies before you is to sort out the root cause of the problem you have. Most often you will find them to be related to the YARN architecture that is used by Hadoop.YARN is static in nature and after the scheduling of jobs the process of adjusting system and network resources. The solution lies in configuring YARN to deal with worst case scenarios.
Jobs with High Priority Fail to Finish on Time
All jobs running on clusters are not equally important and there may be present jobs with critical importance that must be completed within a given time frame. And one might find himself in a situation where jobs of high priority are not finishing within the stipulated deadlines.Troubleshooting such problems may be begun by checking parameters or configuration setting that have been modified in the recent past. You may also ask other users of the same cluster whether they have tweaked with settings or applications. This approach is time consuming and not all users will necessary provide all of the information. Up- front planning holds the key to resolve such sorts of resource contention.
You Cluster Halts Occasionally
In order to solve problems of this type node monitoring tools often fail to make the grade as their visibility cannot be broken down to the level of users, tasks or jobs. An alternative approach to resolve the problem remains that tools like iostat which monitor all of the processes that use disks significantly. Still you need to anticipate spikes in the usage of disks through such methods and it may not be completed by relying solely on human interaction and technology must be used. It is advisable that you invest in tools that automatically correct any contention problem even while jobs are in progress. Hadoop’s value may be maximized through anticipation of, reacting swiftly to and making real tiomedecisions.
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